bioRxivEvolutionary BiologyPreprint
Integrating genomic and tagging data reveals spatio-temporal population structure in Northeast Atlantic European sea bass
Pierre‐Alexandre GagnaireiD, Mathieu WoilleziD, Hélène de PontualiD
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Abstract
Understanding spatial and temporal connectivity among individuals with different migration strategies is essential for migratory ecology and effective conservation, yet it often requires integrating multiple data sources. In Northeast Atlantic European sea bass (Dicentrarchus labrax), electronic tagging has revealed partial migration, with both resident and long-distance migrants showing fidelity to summer feeding and winter spawning areas. However, the role of regional spawning-site philopatry in shaping migration patterns and stock connectivity remains unclear. Here, we combine reconstructed migration trajectories with genome-wide analyses of gene flow and recent relatedness in 708 individuals sampled from 10 French Atlantic locations. We identify a seasonally shifting genetic discontinuity between the Bay of Biscay (BOB) and Northern (NS) stocks, located off western Brittany during winter spawning and displaced northeastward into the central English Channel during summer feeding. Despite seasonal mixing in the English Channel, an association between individual genetic composition and spawning-site selection supports regional spawning-site philopatry. Analyses of long genomic segments shared identical-by-descent reveal substantially greater connectivity within stocks than between stocks, indicating that philopatry constrains effective gene flow despite seasonal mixing. Reanalysis of independent genomic data further shows that sea bass from the northern Atlantic range predominantly belong to the Northern stock. Together, these results show how seasonal movements reshape spatial genetic structure while maintaining demographic subdivision, with direct implications for fisheries management.
- organism: non-human primate
- assay: DNA methylation
- research focus: cell type annotation
- approach: statistical model
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